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Cornerstone I: Representing Knowledge

Cornerstone I: Representing Knowledge. From Data to Knowledge Through Concept-Oriented Terminologies James J. Cimino. The first step on the path to knowledge is getting things by their right names. -Chinese saying. Overview. What is “data to knowledge”? Knowledge representation choices

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Cornerstone I: Representing Knowledge

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  1. Cornerstone I: Representing Knowledge From Data to Knowledge Through Concept-Oriented Terminologies James J. Cimino

  2. The first step on the path to knowledge is getting things by their right names. -Chinese saying

  3. Overview • What is “data to knowledge”? • Knowledge representation choices • Knowledge-based terminology efforts • Medical Entities Dictionary • Proof of concepts

  4. What is “data to knowledge”? • Start with patient data in the medical record • Enhance knowledge by: • gaining a better understanding of the patient • learning relevant knowledge • bringing smart systems to bear to apply knowledge • discovering new knowledge from health data

  5. Knowledge Representation • Terminology for representing symbols • Format for arranging the symbols

  6. Knowledge Representation Choices • Guideline implementation

  7. Guideline Implementation • Starren and Xie, SCAMC, 1994 • National Cholesterol Education Panel Guideline

  8. Cholesterol 200 to 239 Cholesterol <200 Cholesterol >239 Cholesterol 200 to 239 HDL <35 or 2 Risks HDL >35, <2 Risks HDL >35, <2 Risks Provide dietary information Reevaluate in 2 years National Cholesterol Education Panel Guideline Measure Cholesterol & Assess Risk Factors

  9. Guideline Implementation • Starren and Xie, SCAMC, 1994 • National Cholesterol Education Panel Guideline • Three representations: • PROLOG (first-order logic)

  10. NCEP Guideline in PROLOG rule_j(PID):- check_lab(PID,hdl,HDL,_),!, HDL >= 35, total_risk(PID,Risk),!, Risk < 2, check_lab(PID,cholesterol), C,_), C >= 200, C =< 239, print_rule_j.

  11. Guideline Implementation • Starren and Xie, SCAMC, 1994 • National Cholesterol Education Panel Guideline • Three representations: • PROLOG (first-order logic) • CLASSIC (frames)

  12. NCEP Guideline in CLASSIC (CL-DEFINE-CONCEPT ‘C-PATIENT ‘(AND (ALL CHOL (AND INTEGER (MIN 200) (MAX 239))))) (CL-DEFINE-CONCEPT ‘G-PATIENT ‘(AND C-PATIENT LOW-RISK-PATIENT (ALL HDL (AND INTEGER (MIN 35)))))

  13. Guideline Implementation • Starren and Xie, SCAMC, 1994 • National Cholesterol Education Panel Guideline • Three representations: • PROLOG (first-order logic) • CLASSIC (frames) • CLIPS (production rules)

  14. NCEP Guideline in CLIPS (defrule C2G2J “Rules to reach box J” ?f1 <- (calculated-patient (state c) (done no) (hdl ?hdl) (name ?name) (test (>= ?hdl 35)) => (printout “Patient “ ?name “needs treatment”)

  15. Guideline Implementation • Starren and Xie, SCAMC, 1994 • National Cholesterol Education Panel Guideline • Three representations: • PROLOG (first-order logic) • CLASSIC (frames) • CLIPS (production rules) • “All three representations proved adequate for encoding the guideline”

  16. Knowledge Representation Choices • Guideline implementation • Terminologic knowledge

  17. Terminology Representation Choices • Frame-based

  18. Frame-Based Representation Serum Glucose Test is-a: Lab Test Measures: Glucose Specimen: Serum Units: “mg/dl”

  19. Terminology Representation Choices Terminology Representation Choices • Frame-based • Semantic network

  20. Chemical Lab Test Body Substance is-a is-a is-a Glucose Serum specimen measures Semantic Network Representation Serum Glucose Test

  21. Terminology Representation Choices Terminology Representation Choices • Frame-based • Semantic network • Conceptual graphs

  22. Conceptual Graph Representation [Serum Glucose Test] - (is-a) -> [Lab Test] (measures) -> [Glucose] (specimen) -> [Serum]

  23. Terminology Representation Choices Terminology Representation Choices • Frame-based • Semantic network • Conceptual graphs

  24. Knowledge Representation Choices • Guideline implementation • Terminologic knowledge

  25. Knowledge Representation • Terminology for representing symbols • Format for arranging the symbols • Terminology and format for representing terminologic knowledge

  26. Knowledge-Based Terminology Efforts • Jochen Bernauer, SCAMC, 1991

  27. femur increased_uptake right site site_attr during bone_phase Jochen Bernauer, SCAMC, 1991 • Conceptual graphs to model findings

  28. Knowledge-Based Terminology Efforts • Jochen Bernauer, SCAMC, 1991 • Rector, Nolan and Glowinski, SCAMC, 1993

  29. Rector, Nolan and Glowinski, SCAMC, 1993 • GALEN project conditions grammatically haveLocation bodyparts fractures sensibly haveLocation bones femurs sensiblyAndNecessarily haveDivision neck

  30. Knowledge-Based Terminology Efforts • Jochen Bernauer, SCAMC, 1991 • Rector, Nolan and Glowinski, SCAMC, 1993 • Campbell and Musen, SCAMC, 1993

  31. Campbell and Musen, SCAMC, 1993 • Conceptual graphs and SNOMED • Pain + Chest + Radiation to + Left + Arm [Pain] - (located in) -> [Chest] (radiating to) -> [Arm] -> (with laterality) -> [Left]

  32. Knowledge-Based Terminology Efforts • Jochen Bernauer, SCAMC, 1991 • Rector, Nolan and Glowinski, SCAMC, 1993 • Campbell and Musen, SCAMC, 1993 • Lindberg, Humphreys, McCray, Methods 1993

  33. Lexical group String String Lindberg, Humphreys, McCray, Methods 1993 • Unified Medical Language System Concept Lexical group String String

  34. Knowledge-Based Terminology Efforts • Jochen Bernauer, SCAMC, 1991 • Rector, Nolan and Glowinski, SCAMC, 1993 • Campbell and Musen, SCAMC, 1993 • Lindberg, Humphreys, McCray, Methods 1993 • Rocha, Huff, et al., CBM, 1994

  35. Rocha, Huff, et al., CBM, 1994 • VOSER • A server architecture for managing terminologic knowledege

  36. Knowledge-Based Terminology Efforts • Jochen Bernauer, SCAMC, 1991 • Rector, Nolan and Glowinski, SCAMC, 1993 • Campbell and Musen, SCAMC, 1993 • Lindberg, Humphreys, McCray, Methods 1993 • Rocha, Huff, et al., CBM, 1994 • Campbell, Cohn, Chute, et al., SCAMC 1996

  37. Campbell, Cohn, Chute, et al., SCAMC 1996 • Convergent Medical Terminology • SNOMED/Kaiser/Mayo • Galapagos

  38. Knowledge-Based Terminology Efforts • Jochen Bernauer, SCAMC, 1991 • Rector, Nolan and Glowinski, SCAMC, 1993 • Campbell and Musen, SCAMC, 1993 • Lindberg, Humphreys, McCray, Methods 1993 • Rocha, Huff, et al., CBM, 1994 • Campbell, Cohn, Chute, et al., SCAMC 1996 • Brown, O’Neil and Price, Methods, 1997

  39. Brown, O’Neil and Price, Methods, 1997 • Read Codes • Representation with GALEN model

  40. Knowledge-Based Terminology Efforts • Jochen Bernauer, SCAMC, 1991 • Rector, Nolan and Glowinski, SCAMC, 1993 • Campbell and Musen, SCAMC, 1993 • Lindberg, Humphreys, McCray, Methods 1993 • Rocha, Huff, et al., CBM, 1994 • Campbell, Cohn, Chute, et al., SCAMC 1996 • Brown, O’Neil and Price, Methods, 1997 • Spackman, Campbell, and Côte, SCAMC 1997

  41. Spackman, Campbell, and Côte, SCAMC 1997 • SNOMED RT (Reference Terminology) • Convergent Medical Terminology • Description Logic Format

  42. Knowledge-Based Terminology Efforts • Jochen Bernauer, SCAMC, 1991 • Rector, Nolan and Glowinski, SCAMC, 1993 • Campbell and Musen, SCAMC, 1993 • Lindberg, Humphreys, McCray, Methods 1993 • Rocha, Huff, et al., CBM, 1994 • Campbell, Cohn, Chute, et al., SCAMC 1996 • Brown, O’Neil and Price, Methods, 1997 • Spackman, Campbell, and Côte, SCAMC 1997 • Huff, Rocha, McDonald, et al., JAMIA 1998

  43. Huff, Rocha, McDonald, et al., JAMIA 1998 • Logical Observations, Identfiers, Names and Codes (LOINC) 4764-5 | GLUCOSE^3H POST 100 G GLUCOSE PO | SCNC | PT | SER/PLAS | QN|

  44. Knowledge-Based Terminology Efforts • Jochen Bernauer, SCAMC, 1991 • Rector, Nolan and Glowinski, SCAMC, 1993 • Campbell and Musen, SCAMC, 1993 • Lindberg, Humphreys, McCray, Methods 1993 • Rocha, Huff, et al., CBM, 1994 • Campbell, Cohn, Chute, et al., SCAMC 1996 • Brown, O’Neil and Price, Methods, 1997 • Spackman, Campbell, and Côte, SCAMC 1997 • Huff, Rocha, McDonald, et al., JAMIA 1998 • Pharmacy system knowledge base vendors

  45. Drug Class International Package Identifiers is-a Not-Fully-Specified Drug is-a Ingredient Class is-a Clinical Drug is-a is-a is-a Composite Clinical Drug Trademark Drug is-a is-a Pharmacy System Knowledge Base Vendors Country-Specific Packaged Product Ingredient Manufactured Components Composite Trademark Drug

  46. Knowledge-Based Terminology Efforts • Jochen Bernauer, SCAMC, 1991 • Rector, Nolan and Glowinski, SCAMC, 1993 • Campbell and Musen, SCAMC, 1993 • Lindberg, Humphreys, McCray, Methods 1993 • Rocha, Huff, et al., CBM, 1994 • Campbell, Cohn, Chute, et al., SCAMC 1996 • Brown, O’Neil and Price, Methods, 1997 • Spackman, Campbell, and Côte, SCAMC 1997 • Huff, Rocha, McDonald, et al., JAMIA 1998 • Pharmacy system knowledge base vendors

  47. Medical Entities Dictionary (MED) • New York Presbyterian Hospital • 60,000 concepts (procs, results, drugs, probs) • 208,242 synonyms • 84,677 hierarchical links • 113,906 semantic links • 238,040 other attributes • 66,404 translations (ICD9-CM, LOINC, MeSH, UMLS)

  48. Central Controlled Terminology

  49. MED Data Structures • Semantic network

  50. Substance Laboratory Specimen Event Chemical Anatomic Substance Plasma Specimen Diagnostic Procedure Substance Sampled Plasma Laboratory Test Laboratory Procedure Has Specimen Carbo- hydrate Bioactive Substance CHEM-7 Part of Glucose Substance Measured MED Semantic Network Medical Entity Plasma Glucose

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